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Rapid approach for assessing an unregulated fishery using a series of data‐limited tools

Marine and Coastal Fisheries

October 2023
15(5)

DOI:10.1002/mcf2.10270

License
CC BY 4.0

Authors:

Mandy Karnauskas

National Oceanic and Atmospheric Administration

Adyan Rios

National Oceanic and Atmospheric Administration

Rolando O. Santos

Florida International University

Show all 7 authorsHide

Objective Fisheries provide countless benefits to human populations but face many threats ranging from climate change to overfishing. Despite these threats and an increase in fishing pressure globally, most stocks remain unassessed and data limited. An abundance of data‐limited assessment methods exists, but each has different data requirements, caveats, and limitations. Furthermore, developing informative model priors can be difficult when little is known about the stock, and uncertain model parameters could create misleading results about stock status. Our research illustrates an approach for rapidly creating robust initial assessments of unregulated and data‐limited fisheries without the need for additional data collection. Methods Our method uses stakeholder knowledge combined with a series of data‐limited tools to identify an appropriate stock assessment method, conduct an assessment, and examine how model uncertainty influences the results. Our approach was applied to the unregulated and data‐limited fishery for Crevalle Jack Caranx hippos in Florida. Result Results suggested a steady increase in exploitation and a decline in stock biomass over time, with the stock currently overfished and undergoing overfishing. These findings highlight a need for management action to prevent continued stock depletion. Conclusion Our approach can help to streamline the initial assessment and management process for unregulated and data‐limited stocks and serves as an additional tool for combating the many threats facing global fisheries.

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Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science. 2023;15:e10270.

1 of 22

https://doi.org/10.1002/mcf2.10270

wileyonlinelibrary.com/journal/mcf2

Received: 18 November 2022

Revised: 20 July 2023

Accepted: 3 August 2023

DOI: 10.1002/mcf2.10270

ARTICLE

Rapid approach for assessing an unregulated fishery using a

series of data- limited tools

Carissa L.Gervasi1

MandyKarnauskas2

AdyanRios2

Rolando O.Santos1

W. RyanJames1

Ryan J.Rezek3

Jennifer S.Rehage1

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided

the original work is properly cited.

1Institute of Environment, Florida

International University, Miami,

Florida, USA

2National Oceanic and Atmospheric

Administration, Southeast Fisheries

Science Center, Key Biscayne, Florida,

USA

3Department of Marine Science,

Coastal Carolina University, Conway,

South Carolina, USA

Correspondence

Carissa L. Gervasi

Email: carissa.gervasi@noaa.gov

Funding information

National Science Foundation, Grant/

Award Number: HRD- 1547798 and

HRD- 2111661

Abstract

Objective: Fisheries provide countless benefits to human populations but face

many threats ranging from climate change to overfishing. Despite these threats

and an increase in fishing pressure globally, most stocks remain unassessed and

data limited. An abundance of data- limited assessment methods exists, but each

has different data requirements, caveats, and limitations. Furthermore, developing

informative model priors can be difficult when little is known about the stock, and

uncertain model parameters could create misleading results about stock status. Our

research illustrates an approach for rapidly creating robust initial assessments of un-

regulated and data- limited fisheries without the need for additional data collection.

Methods: Our method uses stakeholder knowledge combined with a series of

data- limited tools to identify an appropriate stock assessment method, conduct

an assessment, and examine how model uncertainty influences the results. Our

approach was applied to the unregulated and data- limited fishery for Crevalle

Jack Caranx hippos in Florida.

Result: Results suggested a steady increase in exploitation and a decline in stock

biomass over time, with the stock currently overfished and undergoing overfish-

ing. These findings highlight a need for management action to prevent continued

stock depletion.

Conclusion: Our approach can help to streamline the initial assessment and

management process for unregulated and data- limited stocks and serves as an

additional tool for combating the many threats facing global fisheries.

KEYWORDS

CMSY, Crevalle Jack, data limited, FishPath, local ecological knowledge, stock assessment

INTRODUCTION

Despite their importance, the status of many global fish-

eries remains unknown or poorly estimated due to a lack

of sufficient data or institutional capacity required to con-

duct traditional stock assessments (Cope et al. 2023). The

majority of global fisheries are lacking formal assessment,

and studies have estimated that these unassessed fisher-

ies may be in significantly worse condition than assessed

2 of 22

GERVASI et al.

fisheries (Costello et al.2012; Blasco et al.2020; Hilborn

et al. 2020). Furthermore, due to increasing fishing pres-

sure and constraints on fisheries management programs,

the development of monitoring and assessment plans for all

harvested fish species is an unattainable goal (Harford and

Carruthers2017; Sagarese et al.2019). Although significant

progress has been made toward improving fisheries data col-

lection (e.g., Bryan et al.2016; Amoroso et al.2018; Rousseau

et al.2019), there will likely continue to be a need for alter-

native, data- limited approaches to stock assessment in the

future (Sagarese et al. 2019). This is particularly true for

areas like the southeastern United States, a highly biodiverse

region where fisheries are dominated by the recreational

sector (Shertzer et al.2019) and where over 75% of stocks are

considered data limited (i.e., lacking sufficient data to con-

duct traditional assessments; Berkson and Thorson 2015;

Newman et al.2015). There is an urgent need for (1) rapid

assessment and management action that can keep pace with

increasing fishing pressure and (2) methods that can iden-

tify unregulated and data- limited fisheries that are at risk of

overexploitation and depletion (Sun et al.2020).

Over the past few decades, numerous data- limited

assessment methods have emerged to tackle this issue

(Dowling et al.2015). Rather than relying on traditional

quantitative, model- based stock assessments, these meth-

ods estimate the status of fish stocks by using a range of

approaches from expert judgment to multiple indicator

models (Dowling et al. 2019). However, methods differ

greatly in their data requirements, caveats, and context,

making it difficult to determine which assessment method

is the best choice for a particular fishery. Blanket applica-

tion of generic models can lead to an inaccurate portrayal

of fishery status and trends, thereby hindering effective

management (Dowling et al. 2019). This is because using

generic methods without first assessing whether they are

suitable for the fishery of interest increases the likelihood

of violating model assumptions and overlooking biases or

other data quality issues. Fortunately, several decision sup-

port tools have been developed in recent years that aim to

assist fisheries scientists, managers, and stakeholders in de-

termining the appropriate methods for assessing and man-

aging a given fishery (McDonald et al.2018). One example

is the FishPath tool (www.fishp ath.org), a decision support

tool that was developed in 2016 and helps to guide users

through the selection of appropriate methods for monitor-

ing, assessment, and management of data- limited fisheries

(Dowling et al.2016). The FishPath online assessment tool

contains a repository of data requirements and assump-

tions for over 50 stock assessment methods, with a focus

on data- limited options (Fitzgerald et al. 2018; Dichmont

et al.2021). Users first characterize their fishery via a series

of multiple- choice questions concerning biological and

life history attributes, fishery operational characteristics,

data availability, socioeconomic factors, and governance

context. The answers to these questions are then used to

identify possible assessment and management options that

are best suited to the fishery. Using a standardized tool like

FishPath can provide consistency and objectivity to data-

limited fisheries management and has the potential to be-

come a key resource for the assessment and management

of unregulated species (Fitzgerald et al.2018).

In addition to the development of numerous alternative

approaches to traditional stock assessment, fisheries science

is increasingly using stakeholder local ecological knowledge

(LEK) to help identify conservation concerns (Silvano and

Valbo- Jørgensen2008; Gervasi et al.2022b), estimate trends

in stock status over time (Beaudreau and Levin2014; Kroloff

et al. 2019), improve fisheries models (Bélisle et al. 2018),

and fill in critical knowledge gaps about species biology and

ecology (Anadón et al.2010). Local ecological knowledge is

the in- depth knowledge of the local natural environment ob-

tained by individuals or groups of people through personal

observations, practical experience, and community dialog

(Anadón et al.2009). Research has shown that angler LEK

can complement biological data and provide new insights

(Silvano et al.2008; Cardoso da Silva et al.2020). For example,

Figus et al.(2017) showed that both fishermen and scientists

observed similar declines in the abundance and condition

of Atlantic Cod Gadus morhua in the eastern Baltic Sea,

Poland. In addition to this consensus, fisher LEK revealed

a potential driver of the decline that was at odds with the

findings of scientists, prompting additional avenues for re-

search. There are several examples of angler LEK being used

to directly inform fisheries management, including develop-

ing management options with a high probability of success

and compliance (Heyman and Granados- Dieseldorff2012),

understanding causes of disagreement with existing man-

agement measures and differing stakeholder preferences

(Hill et al.2010; Figus and Criddle2019), developing fishery

surveillance indicators that can be used to continually mon-

itor fisheries (Shephard et al.2021), and providing estimates

of model parameters used in stock assessments (Ainsworth

and Pitcher2005; Beaudreau and Levin 2014; Friedlander

et al.2015). Although these studies demonstrate clear bene-

fits to incorporating angler LEK into fisheries management,

Impact statement

This study presents a method for conducting

rapid, low- cost fish stock assessments that was

applied to the Crevalle Jack fishery in Florida.

Application of this method to unregulated fish

species can help managers better assess fish stocks

and conserve important fisheries.

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ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

there has been a lack of standardized protocols and methods

for doing so (Hind2015) and integration of LEK into bio-

logical assessments remains uncommon (Figus et al.2017).

The goal of this study was to develop an approach for

conducting rapid initial assessments (i.e., using only ex-

isting information sans additional data collection) of un-

regulated and data- limited fisheries that could be applied

to the Crevalle Jack Caranx hippos fishery in Florida. The

Crevalle Jack is a large marine species that is targeted by

both commercial and recreational anglers, but the fishery

in Florida is currently unregulated and data limited. Fur-

thermore, research has suggested that the population may

be in decline (Gervasi et al.2022b). Our approach used

angler LEK in conjunction with a series of data- limited

assessment tools to assess the current status of the Florida

Crevalle Jack stock, examine trends in stock status and ex-

ploitation over time, and develop initial management ref-

erence points, which are benchmarks that scientists and

managers use to set targets or limits on fishing effort and

to monitor the success of management strategies (Caddy

and Mahon1995). First, we used information gathered

from LEK and other sources to fill out the FishPath as-

sessment questionnaire and to choose a data- limited stock

assessment method that was suited to the fishery of inter-

est. Second, we conducted a stock assessment using the

chosen method, with LEK informing unknown model pa-

rameters and filling in data gaps. Finally, simple sensitiv-

ity analyses were run to test how uncertain or unknown

parameters (including those estimated by LEK) affected

the estimates of stock status (Figure1).

METHODS

Study species

The Crevalle Jack is a large pelagic fish species with a

native range spanning the east coasts of North America

and Central America (Smith- Vaniz and Carpenter 2007).

In Florida, the Crevalle Jack is a popular sport fish spe-

cies that is highly valued by recreational anglers for its

strength and speed (Gervasi et al.2022b). Crevalle Jack are

also captured in commercial fisheries (mainly as bycatch)

throughout the state but are unregulated and understud-

ied. The Florida Fish and Wildlife Conservation Commis-

sion makes management decisions for all fisheries within

state waters; such decisions include setting gear restric-

tions, size limits, and bag limits and instituting closed sea-

sons. State waters extend 4.83 km (3 mi) from shore on the

east coast of Florida and 16.09 km (10 mi) from shore on

Florida's west coast (Figure2). Several species (including

the Crevalle Jack) are listed as unregulated species in the

state, which means that they have no specific regulations

regarding gear restrictions, size limits, bag limits, or closed

seasons. Florida does, however, have a default limit of two

fish or 45.36 kg (100 lb) per person per day (whichever is

greater) for all unregulated species (Florida Fish and Wild-

life Conservation Commission [FWC]2021). Crevalle Jack

are found in a variety of habitats, including offshore reefs

(Smith- Vaniz and Carpenter 2007). Hence, they are also

captured in federal waters within the U.S. Exclusive Eco-

nomic Zone (Figure2). Federal fisheries in the region are

managed by the National Oceanic and Atmospheric Ad-

ministration (NOAA) Fisheries, the South Atlantic Fish-

ery Management Council, and the Gulf of Mexico Fishery

Management Council (NOAA– Fisheries 2021). Crevalle

Jack are not currently managed as a federal species, so

there are no restrictions on their harvest in federal waters.

Due to the Crevalle Jack's unregulated status, limited

research has been done in the region to assess the species'

life history (e.g., stock boundaries and migration patterns)

or fishery trends (e.g., trends in length and age compo-

sition; McBride and McKown2000; Gervasi et al. 2022b;

Jefferson et al.2022). Therefore, the Crevalle Jack fishery

in Florida can be considered data limited, a term that gen-

erally describes situations in which the data required to

support a fully integrated stock assessment model (includ-

ing catch time series, indices of abundance, length and

age composition, and life history parameters) are missing

(Cope et al. 2023). In the Florida Keys, recreational fish-

ing guides have observed a concerning decline in Crevalle

Jack catch rates, which is supported by available fisheries-

dependent data (Gervasi et al. 2022b). This decline

prompts a pressing need for assessment of the species in

Florida and possible future management action. The Flor-

ida Crevalle Jack fishery is therefore an ideal candidate for

applying the methodology outlined herein.

The FishPath tool

In this study, the FishPath assessment questionnaire was

filled out by the lead author for Florida Crevalle Jack by

using information compiled from various sources (Table1;

File S1 available in the Supplement separately online).

When possible, published literature and existing fisheries-

dependent data were used to answer the multiple- choice

questions. However, some questions could not be an-

swered without additional research or data collection. In

these instances, LEK was used to fill in the knowledge

gaps. Fortunately, LEK data concerning the Crevalle Jack

population in south Florida were already available from

interviews conducted in 2019 with expert recreational fish-

ing guides in the Florida Keys (Gervasi et al.2022b). Most

of the remaining FishPath questions could be answered

using this existing LEK data set. Interview methods are

4 of 22

GERVASI et al.

described in depth by Gervasi et al.(2022b). Briefly, key

informant interviews were conducted with 18 veteran

charter- for- hire captains in the Florida Keys, where a de-

cline in Crevalle Jack catches had been observed. Captains

were asked a series of open- ended questions to guide the

conversations: (1) “What is your general background and

experience fishing and guiding?”; (2) “What do you know

about Crevalle Jack?”; (3) “Have you noticed any changes

in Crevalle Jack fishing over time?”; and (4) “Is fishing for

Crevalle Jack important to you?” More specific follow- up

questions were asked as needed, with the goal of capturing

perceptions about the Crevalle Jack fishery and stock sta-

tus as well as gaining an understanding of how and why

stakeholders interact with the species (FigureS1 available

in the Supplement separately online). Common percep-

tions of the fish population and fishery among anglers

were summarized and used in the current study to fill

out the FishPath questionnaire. All protocols for human

subject research were approved by Florida International

University's (FIU) Institutional Review Board, and all par-

ticipants gave consent before being interviewed. Any re-

maining FishPath questions were answered by consulting

FIGURE  Framework presented in this paper for conducting rapid initial assessments of unregulated and data- limited fisheries using

a three- prong approach, with angler local ecological knowledge (LEK) permeating each step. First, data from LEK and other sources are

used to fill out the FishPath assessment questionnaire and choose a data- limited stock assessment method suited to the fishery of interest.

Second, a stock assessment is conducted for the species of interest by using the chosen method, with LEK informing unknown model

parameters. Finally, simple sensitivity analyses are run to test how uncertain or unknown parameters (including those estimated by LEK)

affect estimates of stock status. Highly influential parameters highlight critical future research needs.

5 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

with the previously interviewed anglers and asking them

to address the specific question.

The FishPath assessment questionnaire includes five cat-

egories of questions concerning the biology and life history

of the species, data availability, governance, management,

and operational characteristics. All available fisheries-

dependent and fisheries- independent surveys that operate

in Florida and regularly encounter Crevalle Jack were com-

piled to answer questions about data availability. Previous

literature on the Crevalle Jack was used to inform ques-

tions about biology and life history (Smith- Vaniz and Car-

penter2007; Caiafa et al.2011; Alfaro- Martínez et al.2016;

Jefferson et al.2022). Common perceptions of the Crevalle

Jack fishery from LEK interviews were summarized to gen-

erate a LEK data set for use in the FishPath assessment and

the resulting selected model. The LEK data included infor-

mation on relative stock status and the nature of fishery op-

erations, including targeting, species uses, and fishing areas

(Table1; FileS1).

The FishPath assessment tool does not rank the possi-

ble assessment methods, but it does filter out any methods

for which the minimum data requirements or criteria are

not met based on the questionnaire responses (Dowling

et al.2016). The tool also displays “traffic light” caveats that

highlight each possible assessment method's major assump-

tions and data requirements as they relate to the fishery of

interest. Caveats that are red are important assumptions that

might not be met according to the questionnaire responses,

so those methods should be used with extreme caution. All

options with one or more red caveats were eliminated. Fish-

Path also ranks each method by assessment tier (i.e., model

complexity), with tiers ranging from simple, extremely data-

limited methods (pre- assessment) to robust methods that

require additional data (high tier). Options were sorted by

assessment tier, and the highest tier options that remained

after the elimination of options with red caveats were con-

sidered the best options for assessment of the Florida Cre-

valle Jack fishery (FileS2).

FIGURE  Map of the study area in Florida, highlighting state water boundaries (4.83 km [3 mi] from shore on the east coast; 16.09 km

[10 mi] from shore on the west coast) and the U.S. federal Exclusive Economic Zone (EEZ). The inset map highlights the study area in the

southeastern United States. (State boundary shapefile was downloaded from FWC- Fish and Wildlife Research Institute2007; federal EEZ

shapefile was downloaded from Flanders Marine Institute2019).

6 of 22

GERVASI et al.

Model inputs

Model priors

All methods pertaining to model inputs were contingent

upon the results of using the FishPath tool. We chose

the highest tier type of assessment recommended: the

CMSY– BSM model (CMSY = catch– maximum sustainable

yield [MSY]; BSM = Bayesian state- space implementation of

the Schaefer surplus production model; Froese et al.2017,

2019). Details of this selection are presented in the Results

(Assessment model selection). The CMSY– BSM model re-

quires a time series of total fishery removals (hereafter re-

ferred to as “catch”); priors for resilience (defined as species

TABLE  Available data and information about Crevalle Jack that were compiled and used to fill out the FishPath assessment

questionnaire. FL, fork length; FWC, Fish and Wildlife Conservation Commission; GOM, Gulf of Mexico; LEK, local ecological knowledge;

MRIP, Marine Recreational Information Program; NOAA, National Oceanic and Atmospheric Administration.

Data category Variable Estimate Source

Biological and life

history attributes

Minimum size at maturity ~40 cm FL Caiafa et al.2011 (Colombia)

Maximum size ~100 cm FL Jefferson et al.2022 (GOM)

Longevity 20 years Jefferson et al.2022 (GOM)

Size at 50% maturity 62.6 cm FL (males); 66.2 cm FL (females) Caiafa et al.2011 (Colombia)

Length– weight

log10(weight)=−16.47 +2.79

⋅

log10(FL)

Jefferson et al.2022 (GOM)

Growth rate (females)

(F)

=903.04

[

1−e

−0.39(t−0.73)]

Jefferson et al.2022 (GOM)

Growth rate (males)

lt(M)

=887.16

[

1−e−

0.39

(

−

0.73

)

]

Jefferson et al.2022 (GOM)

Length– fecundity Relationship not significant Alfaro- Martínez et al.2016

(Colombia)

Natural mortality rate 1.12 year−1 Caiafa et al.2011 (Colombia)

Fishing mortality rate 0.63 year−1 Caiafa et al.2011 (Colombia)

Stock boundaries Single Florida stock Preliminary acoustic telemetry data

(C. L. Gervasi, unpublished

data)

Habitat use Variable Smith- Vaniz and Carpenter2007;

fishing guide LEK (Gervasi

et al.2022b)

Available data Commercial landings Available for the entire state of Florida

from 1950 to 2021

Florida FWC

Commercial effort Number of landings receipts can be a proxy Florida FWC

Commercial size composition Size/age composition data are not available N/A

Recreational landings Available for the entire state of Florida

from 1981 to 2021

NOAA MRIP dockside survey

Recreational effort Estimated via household mail survey NOAA Fishing Effort Survey

Recreational size composition Subset of size data available for landed fish NOAA MRIP dockside survey

Fisheries independent No suitable surveys in the region N/A

Fishery attributes and

management

Stock status High in the 1980s; gradual decline since Fishing guide LEK (Gervasi

et al.2022b)

Commercial gear used Cast net, hook and line, gill net = 67% Commercial data

Recreational gear used Hook and line = 99% Recreational data

Discards >80% of recreational catch discarded annually Recreational data

Discard mortality rate 10% Fishing guide LEK (guides were

consulted specifically for this

study)

Targeting Opportunistic; some targeting Fishing guide LEK (Gervasi

et al.2022b)

Management Unregulated Florida FWC

7 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

productivity or resilience to fishing); priors for biomass B

relative to carrying capacity k (i.e., B/k) at the beginning,

middle, and end of the catch time series; and an optional

biomass time series. For Crevalle Jack, prior estimates for

the maximum intrinsic rate of population increase (r) were

extracted from the “Estimates based on models” section of

FishBase using a species resilience category of “medium”

(Smith- Vaniz et al. 1990; Froese and Pauly2021). A prior

range for k was derived by the model from the maximum

catch. The available time series of recreational fishing effort

used to develop an index of Crevalle Jack abundance did not

cover the entire time series of fishing effort. Therefore, fish-

ing guide LEK was used to determine prior range categories

for B/k at the beginning, middle, and end of the time series

based on guide estimates of how population abundance has

changed over time. The default B/k ranges corresponding to

these categories from Froese et al.(2017) were used in the

model (Table2).

Catch time series

The following formula was used to create a time series of

total fishery removals (catch time series) for Crevalle Jack

in Florida from 1950 to 2021:

where t is year and “discard mortality” is an estimated

discard mortality rate (Figure3). Discard mortality occurs

when fish are caught and released alive but die after release

due to injuries suffered from the angling encounter or due

to an increased susceptibility to predation (Rudershausen

et al. 2007). The discard mortality rate is defined as the

proportion of individuals that suffer from discard mortal-

ity and therefore can be considered a component of fish-

ery removals. Previously interviewed fishing guides were

asked to estimate a discard mortality rate. The average of

their responses was used as the base discard mortality rate

in this study. Preliminary acoustic telemetry research has

revealed population connectivity of Crevalle Jack through-

out the state of Florida (C. L. Gervasi, unpublished data).

Hence, we assumed that state- level boundaries provided a

reasonable approximation of the stock unit. All catch data

were thus collected for the entire state. Commercial land-

ings data were obtained from the National Marine Fisheries

Service's Accumulated Landings System (NOAA 2021a).

Landings data for Crevalle Jack were downloaded for all

of Florida from the beginning of the time series (1950) to

the last available year (2021). Crevalle Jack recreational

landings (fish that were brought back to shore) and dis-

cards (fish that were caught and released either dead or

alive) for the state of Florida were downloaded from

the NOAA Fisheries' Marine Recreational Information

Program (MRIP) online query tool for the period of record

from 1981 to 2021 (NOAA 2021b). Additional details on

how commercial and recreational landings data were ob-

tained and how the discard mortality rate was calculated

are available in FileS3.

Abundance time series

No fisheries- independent surveys are operating in the

region that regularly encounter adult Crevalle Jack, but

relative abundance trends can be inferred from catch- per-

unit- effort (CPUE) data (Maunder and Punt 2004). For

the purposes of this study, we used the MRIP CPUE data

subset for Florida to create an index of Crevalle Jack abun-

dance for the entire state. Numerous factors besides stock

abundance can influence fishery catch rates (e.g., spatial,

temporal, and environmental variability), so we stand-

ardized the CPUE data for Crevalle Jack in Florida using

generalized linear models (GLMs; Matthews 2014). Spe-

cifically, a delta- lognormal GLM approach (Lo et al.1992)

was applied, with the following categorical factors in-

cluded in the model: year (1991– 2021), season (spring:

March, April, and May; summer: June, July, and August;

fall: September, October, and November; winter: Decem-

ber, January, and February), fishing mode (shore, charter,

or private), and day (weekday or weekend). Additional de-

tails on standardization methods can be found in Gervasi

et al. (2022b). Filtered and cleaned MRIP data included

240,712 trips from 1991 to 2021 (31 years). Of these trips,

Crevalle Jack were caught during 38,825 trips (16%). Based

on model selection via backward stepwise regression and

deviance tables, the final model for the proportion posi-

tive GLM included year and season as fixed factors, and

the final model for the positive trip GLM included year,

season, and fishing mode as fixed factors (TablesS1 and

S2 available in the Supplement separately online).

Sensitivity analyses

To examine the sensitivity of our Crevalle Jack CMSY–

BSM model to unknown or poorly estimated parameters,

we conducted a series of sensitivity analyses and compared

stock status as well as biomass and exploitation trends to

those generated from our base model (Table2). We ex-

plored seven different scenarios that tested the model sen-

sitivity to potential uncertainty by varying the estimated

discard mortality rate instead of using the LEK- derived

value (analysis 1); ignoring the CPUE data and using only

the CMSY model without the surplus production model

(1)

Commercial landings

recreational landings

(

recreational discards

×discard mortality

)

8 of 22

GERVASI et al.

TABLE  Model inputs and parameters for the base Crevalle Jack stock assessment model and for the seven sensitivity analyses. B, biomass; CMSY, catch–maximum sustainable yield;

CPUE, catch per unit effort; ENP, Everglades National Park; k, carrying capacity; LEK, local ecological knowledge; MRIP, Marine Recreational Information Program; PSE, proportional

standard error; r, intrinsic population growth rate.

Variable Base model

Sensitivity

analysis 1

Sensitivity

analysis 2

Sensitivity

analysis 3

Sensitivity

analysis 4

Sensitivity

analysis 5

Sensitivity

analysis 6

Sensitivity

analysis 7

Description of sensitivity

analysis

– Varying the

discard mortality

rate

Excluding CPUE

data (CMSY

model only)

Excluding

historical catch

Excluding high-

PSE points in

MRIP data

Using

uninformed

priors versus

LEK priors

Using an

alternative

abundance data

set

Accounting for

effort creep

Catch start year 1950 1950 1950 1981 1950 1950 1950 1950

Catch end year 2021 2021 2021 2021 2021 2021 2020 2021

Abundance time series MRIP MRIP N/A MRIP MRIP MRIP ENP MRIP

Resilience categoryaMedium Medium Medium Medium Medium Medium Medium Medium

Prior ranges for ra0.35– 0.80 0.35– 0.80 0.35– 0.80 0.35– 0.80 0.35– 0.80 0.35– 0.80 0.35– 0.80 0.35– 0.80

Relative biomass category

(beginning)b

Nearly unexploited Nearly

unexploited

Nearly unexploited Low depletion Nearly

unexploited

Uninformed Nearly

unexploited

Nearly

unexploited

Prior range for B/k

(beginning)b

0.75– 1.00 0.75– 1.00 0.75– 1.00 0.4– 0.8 0.75– 1.00 0.01– 1.00 0.75– 1.00 0.75– 1.00

Relative biomass category

(middle)b

Low depletion Low depletion Low depletion Low depletion Low depletion Uninformed Low depletion Low depletion

Prior range for B/k

(middle)b

0.4– 0.8 0.4– 0.8 0.4– 0.8 0.4– 0.8 0.4– 0.8 N/A 0.4– 0.8 0.4– 0.8

Relative biomass category

(end)b

Medium depletion Medium depletion Medium depletion Medium

depletion

Medium

depletion

Uninformed Medium depletion Medium

depletion

Prior range for B/k (end)b0.2– 0.6 0.2– 0.6 0.2– 0.6 0.2– 0.6 0.2– 0.6 0.01– 1.00 0.2– 0.6 0.2– 0.6

Effort creep (%) 0 0 0 0 0 0 0 2

Discard mortality rateb0.1 Range from 0.0

to 0.5

0.1 0.1 0.1 0.1 0.1 0.1

Note: Changes to model inputs and parameters from the base model for each sensitivity analysis are shown in bold.

aDerived from FishBase (Froese and Pauly2021).

bValues were derived from angler LEK except in sensitivity analysis 1 (discard mortality not informed by LEK) and sensitivity analysis 5 (priors not informed by LEK).

9 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

(BSM; analysis 2); subsetting the catch time series to begin

at 1981 (the beginning of the MRIP time series), thereby

ignoring the estimated historic recreational catch and his-

toric commercial catch (analysis 3); removing potential

outlier data points from the MRIP time series with high

proportional standard error (PSE; >50%) and replacing

them with interpolated values based on adjacent years

(analysis 4); using uninformed biomass priors instead of

the LEK- derived priors (analysis 5); using an alternative

standardized CPUE time series (developed by Gervasi

et al.2022b) based on the Everglades National Park (ENP)

creel survey (Osborne et al.2006), updated to include data

from 2020, as an index of abundance (analysis 6); and, fi-

nally, incorporating effort creep (2% annual increase in

catchability) into the model based on stock assessments

for other fish species in the Gulf of Mexico (Thorson and

Berkson2010; analysis 7). All analyses were conducted in

R version 4.2.3 (R Core Team 2023).

RESULTS

Assessment model selection

After eliminating FishPath assessment options with red

caveat outputs and sorting by assessment tier (highest to

lowest), two options with the high- tier designation re-

mained (File S2). These options were production models

(e.g., Schaefer, Fox, and Pella– Tomlinson models; Hil-

born and Walters1992) and the qR (catchability– recruits)

method (McGarvey et al. 1997). Production models re-

quire a continuous time series of fishery removals, and the

two major parameters in the models are r and k, which are

used to estimate MSY. Production models additionally re-

quire at least one index of abundance. The qR method uses

time series of catch by weight and in numbers, an estimate

of natural mortality (M), and an average of weight at age

to estimate biomass, catchability, exploitation rate, and

FIGURE  (A) Time series of Crevalle Jack catch (total fishery removals; thousands of metric tons) used in the initial CMSY– BSM

model (defined in Methods) and (B) breakdown of fishery removals by fleet. In panels A and B, an estimated discard mortality rate of 10%

was applied to the recreational live releases to obtain an estimate of recreational dead discards. (C) The Marine Recreational Information

Program (MRIP) standardized abundance index, which was used in the base model and sensitivity analyses 1, 3, 4, 5, and 7 as the biomass

time series, is shown. (D) The Everglades National Park (ENP) standardized abundance index, which was used in sensitivity analysis 6 as the

biomass time series, is presented. Dashed lines in panels C and D are 95% confidence intervals.

Year

All fishery removals (1000 metric tons)

1950 1957 1964 1971 1978 1985 1992 1999 2006 2013 2020

7(A)

Year

All fishery removals (1000 metric tons)

1950 1957 1964 1971 1978 1985 1992 1999 2006 2013 2020

Recreational catch

Commercial catch

Recreational dead discards

(B)

Year

MRIP standardized abundance index

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020

0.0

0.5

1.0

1.5

2.0

2.5 (C)

Year

ENP standardized abundance index

1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 2020

0.0

0.5

1.0

1.5

2.0

2.5 (D)

10 of 22

GERVASI et al.

yearly recruitment. We thoroughly reviewed both meth-

ods in the scientific literature to select the best option for

assessment of the Crevalle Jack fishery. The qR method

was eliminated as an option because catch by numbers is

not recorded for Crevalle Jack in the commercial fishery

and would have had to be estimated. Furthermore, M for

Crevalle Jack in Florida is currently unknown and can-

not be estimated with any certainty (available estimates

are from areas outside the United States). The production

model required fewer inputs of uncertain parameters.

We chose to apply the CMSY– BSM method created

by Froese et al. (2017) and further updated by Froese

et al.(2019). This method includes both the BSM and the

CMSY model, which is similar to a production model but

does not require a time series of abundance. The CMSY–

BSM model was chosen because it estimates biomass, ex-

ploitation rate, MSY, and related fisheries reference points,

with the only data requirements being catch and productiv-

ity (Froese et al.2017). An extensive time series of fishery

removals in Florida is available for Crevalle Jack, and pro-

ductivity information is available from previous research.

The CMSY model is an updated version of the catch– MSY

method originally proposed by Martell and Froese(2013),

which reviews of data- limited assessment methods have

found to be a promising approach (International Council

for the Exploration of the Sea2014; Rosenberg et al.2014).

The predictions of the CMSY– BSM method have been val-

idated against 48 simulated stocks and evaluated against

159 fully or partially assessed real stocks, and estimates

of r, k, and MSY were not significantly different from the

actual values for 90% of simulated stocks and 76% of real

stocks (Froese et al. 2017). Furthermore, a detailed user

manual and R code (last updated in 2019) are available for

download (Froese et al.2017), making the method easily

accessible and reproducible. The updated version of the

model (CMSY+ and BSM; Froese et al.2019) was used in

this study, but for simplicity we refer to it as the “CMSY–

BSM model” hereafter.

Base model run

Our base stock assessment model run for Crevalle Jack

revealed a gradual increase in exploitation and a corre-

sponding gradual decline in stock size from 1950 to 2021

(Figure4). Total catch was below MSY (3155 metric tons

year−1) from 1950 to 1988 and fluctuated around MSY for

the remaining years, with catch being above MSY for 19

of the 33 years from 1989 to 2021. Several definitions for

the terms “overfished” and “overfishing” exist in the lit-

erature, but generally a stock is considered “overfished”

if B is below BMSY by some degree and to be undergo-

ing “overfishing” if fishing mortality F is above FMSY by

some degree (Froese and Proelss 2012, 2013; Langseth

et al.2019; Hilborn2020). For the purposes of this study,

we referred to the Crevalle Jack stock as overfished when-

ever model- estimated B was below BMSY (B/BMSY < 1) and

as undergoing overfishing whenever model- estimated F

was above FMSY (F/FMSY > 1). These definitions do not ac-

count for fluctuations around the thresholds due to inher-

ent variability. A stock that is managed at MSY could be

expected to fluctuate around BMSY. However, the Crevalle

Jack stock is unmanaged and stock size has continually

declined, suggesting that the stock is not being sustain-

ably harvested. According to the base model, F was above

FMSY for 14 of the 22 years since 2000, revealing that over-

fishing has been occurring regularly since 2000. Biomass

was above BMSY from 1950 to 2002 but was below BMSY for

every year from 2003 to 2011 and from 2017 to 2021, with

B being the lowest in 2019. It appears that high levels of

catch above MSY starting in 1989 led to overfishing be-

ginning in 2000 and led to the stock becoming overfished

starting in 2003. According to the assessment, the current

status of the stock is overfished and undergoing overfish-

ing: the estimated F2021/FMSY was 1.12, and the estimated

B2021/BMSY was 0.88 (Table3).

Model sensitivity runs

Our first set of sensitivity runs examined the impact of se-

lecting various discard mortality rates for the recreational

fishery by running a series of models with recreational

discard mortality ranging from 0% to 50% in 10% incre-

ments (Table2; Figure5). Discard mortality rates above

50% were not considered because they were deemed

highly unrealistic by fishing guides (who were asked spe-

cifically about discard mortality for this study). For all

model runs, exploitation in 2021 (F2021/FMSY) was similar,

ranging from 1.1 to 1.3. Stock size in 2021 (B2021/BMSY)

generally increased with an increase in discard mortality,

ranging from 0.81 at 0% mortality to 0.95 at 50% mortality

(Table3; Figure5B). Regardless of the discard mortality

rate used, the models revealed the same trend of gradually

increasing exploitation and gradually decreasing stock

size over time. The status of the stock in 2021 was over-

fished and undergoing overfishing regardless of the mor-

tality rate used. The choice of discard mortality rate had

little effect on the estimate of r in the model but greatly

affected the estimate of k, with k increasing linearly as the

discard mortality rate increased (Figure5C,D). Since k di-

rectly affects the MSY estimate, the estimated MSY also

increased linearly as the discard mortality rate increased.

At a discard mortality rate of 0%, estimated MSY was 2320

metric tons; at a discard mortality rate of 50%, estimated

MSY was 6790 metric tons (Table3).

11 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

The remaining sensitivity analyses (2– 7) examined the

effects of excluding the abundance time series (using the

CMSY model only), excluding historical catch data, ex-

cluding high- PSE data points from the MRIP data, using

uniformed priors versus LEK priors, using an alternative

abundance data set, and accounting for an effort creep

of 2% per year (Table2). Except for sensitivity analy-

ses 5 (uninformed priors) and 6 (alternative abundance

data set), each of the sensitivity model runs revealed the

same pattern of gradually decreasing stock size over time

(Figure6A). Furthermore, estimated B2021 was below BMSY

for all models and estimated F2021 was above FMSY for all

models except in sensitivity analyses 2 and 5 (Table3;

Figure6B). For all model runs, B was below BMSY and F

was above FMSY for at least 4 years of the 71- year time se-

ries (FiguresS3– S8).

Excluding CPUE data (analysis 2) and using unin-

formed biomass priors (analysis 5) led to the most opti-

mistic depictions of current stock status. For analysis 2,

excluding an index of abundance led to a trajectory of

stock status over time similar to that from the base model,

with stock size gradually declining while exploitation

gradually increased over time (FigureS3). For analysis 5,

starting and ending biomass priors (1950 and 2021) were

set to a wide range (0.01– 1.00), which told the model that

we had no information about stock status at the beginning

or the end of the time series (Froese et al.2019). The inter-

mediate biomass level was set to “NA,” which allowed the

model to estimate it from maximum or minimum catch

according to some simple rules (Froese et al.2017). This

version of the model showed a trajectory of gradually in-

creasing exploitation over time, which was the same as the

FIGURE  Summary of information relevant for management of Florida Crevalle Jack from the base CMSY– BSM model (defined

in Methods): (A) catches (total fishery removals; thousands of metric tons per year) relative to maximum sustainable yield (MSY; dashed

line); (B) development of predicted relative total biomass (B/BMSY); (C) relative exploitation (fishing mortality F/FMSY); and (D) trajectory of

relative stock size (B/BMSY) as a function of fishing pressure (F/FMSY). Gray shading in panels A– C denotes 95% confidence limits for MSY,

relative biomass, and relative exploitation, respectively. The oval shape around the assessment of the final year triangle indicates uncertainty

(yellow = 50% confidence interval [CI]; gray = 80% CI; dark gray = 95% CI).

1950 1960 1970 1980 1990 2000 2010 2020

02468

Catch (total fishery removals)

Catch (1000 metric tons/year)

MSY

1950 1960 1970 1980 1990 2000 2010 2020

0.00.5 1.01.5 2.0

Stock size

B/BMSY

1950 1960 1970 1980 1990 2000 2010 2020

0.00.5 1.01.5 2.0

Exploitation

F/FMSY

0.00.5 1.01.5 2.0

Stock status

B/BMSY

F/FMSY

1950

1989

2021

50% CI

80% CI

95% CI

(A)

(B)

12 of 22

GERVASI et al.

base model's trajectory, but it estimated that stock size in

1950 was below BMSY, rapidly increased to high levels in

the 1960s, and then gradually declined (FigureS6). The

most important difference between these models and the

base model was that excluding CPUE data and using un-

informed priors painted a more optimistic picture of stock

status, with the stock in 2021 still just below BMSY but also

below FMSY, suggesting that overfishing is not currently

occurring (Table3; Figure6B). Additionally, exploitation

was above FMSY for only a handful of years during the 71-

year time series.

The most pessimistic models were sensitivity analy-

ses 4 (excluding high- PSE data points) and 7 (including

2% effort creep). Two data points had PSEs above 50%:

1986 and 2009. The catch estimate for 1986 (3107 metric

tons) was similar to the average of the time series. How-

ever, the catch estimate for 2009 (7116 metric tons) was

anomalously high compared to the rest of the time series,

whereas the mean catch before 2009 was 2078 metric tons

(Figure3B). Removing the 2009 data point and replacing

it with an interpolated value brought the total catch for

2009 down to 2787 metric tons. The decrease in total catch

for 2009 had a negative effect on r, with estimated r de-

creasing from 0.55 to 0.49. This resulted in a lower esti-

mated MSY and FMSY and a more pessimistic stock status,

with the stock being more severely overfished and under-

going more severe overfishing in 2021 (Table3; Figure6).

Based on this model, the Crevalle Jack stock was under-

going overfishing for 16 years of the 71- year time series

(FigureS5). Effort creep is defined as some change in

catchability or nominal effort in a fishery over time due to

technological advancements (Palomares and Pauly2019),

such as major improvements in gear design, fish- finding

devices, or vessel capabilities, all of which increase effi-

ciency and therefore impact fishing mortality. The CMSY–

BSM model allows the user to specify a linear annual

increase in catchability, which results in a decrease in

the CPUE index considered by the model. For this sen-

sitivity analysis, a 2% linear increase in catchability was

applied to the MRIP standardized abundance index based

on previous stock assessments in the region (Thorson and

Berkson2010). Although effort creep did not impact the

trajectory of stock status and exploitation over time, it led

to a much steeper decline in stock status since the early

2000s than the base model (FigureS8). Furthermore, es-

timated B was below BMSY for every year since 2003, with

B/BMSY almost as low as 0.5 in 2021, suggestive of a se-

verely overfished stock.

Using an alternative abundance time series (analysis

6) had little effect on estimated management reference

points. However, the ENP time series went back farther

in time than the MRIP time series, and the trajectory of

stock status over time differed slightly between the two

TABLE  Estimated management reference points from the base Crevalle Jack stock assessment model and from the seven sensitivity analyses. B2021, biomass in 2021; BMSY, biomass at

maximum sustainable yield (MSY); CMSY, catch– MSY; CPUE, catch per unit effort; ENP, Everglades National Park; F2021, fishing mortality in 2021; FMSY, fishing mortality at MSY; k, carrying

capacity; LEK, local ecological knowledge; MRIP, Marine Recreational Information Program; PSE, proportional standard error; r, intrinsic population growth rate.

Variable

Base

model

Sensitivity

analysis 1

Sensitivity

analysis 2

Sensitivity

analysis 3

Sensitivity

analysis 4

Sensitivity

analysis 5

Sensitivity

analysis 6

Sensitivity

analysis 7

Description of

sensitivity analysis

– Varying the

discard mortality

rate

Excluding CPUE data

(CMSY model only)

Excluding

historical

catch

Excluding high- PSE

points in MRIP data

Using uninformed

priors versus LEK

priors

Using an alternative

abundance data set

Accounting for effort

creep

r (year−1) 0.55 0.50– 0.55 0.55 0.55 0.49 0.46 0.50 0.45

k (1000 metric tons) 22.9 17.8– 54.2 22.9 23.4 24.3 29.0 25.1 27.8

MSY (1000 metric

tons)

3.15 2.32– 6.79 3.44 3.21 2.96 3.31 3.14 3.11

FMSY (year −1) 0.28 0.25– 0.28 0.31 0.28 0.24 0.23 0.25 1.81

BMSY (1000 metric

tons)

11.4 8.89– 27.1 11.3 11.7 12.2 14.5 12.6 13.9

F2021/FMSY 1.12 1.10– 1.13 0.93 1.10 1.32 0.95 1.04 1.81

B2021/BMSY 0.88 0.81– 0.95 0.99 0.88 0.79 0.99 0.87 0.55

F2021 (year−1) 0.31 0.28– 0.31 0.28 0.30 0.32 0.22 0.26 0.41

13 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

models. With the ENP time series as an index of abun-

dance, stock size declined rapidly from 1970 to 1985 be-

fore increasing back to historic levels and then gradually

declining from 1990 to 2020 in the same fashion as the

base model (FigureS7). For the years in which the two

abundance time series overlapped (1991– 2020), model re-

sults were very similar.

DISCUSSION

In this paper, we demonstrated how a variety of data-

limited tools, when used in combination, can aid in de-

veloping rapid yet robust assessments for data- limited,

unregulated fisheries, thus providing a basis for initial

management. Our approach took advantage of LEK

to inform both model selection and analysis. We used

LEK and other existing data sources to fill out the Fish-

Path assessment questionnaire, which is a currently

underutilized program that provides a transparent,

standardized approach for selecting an appropriate

stock assessment model. Local ecological knowledge

was then used again to parameterize the chosen model

when parameter estimates were unavailable from previ-

ous literature, which is the case for many data- limited

fisheries. Finally, by identifying unknown and uncertain

parameters and running sensitivity analyses to test their

effects on estimates of stock status, we developed some

clear goals and priorities for future research, which will

help to ensure that funding and effort are invested in the

greatest needs. The results of applying our framework

to assessing stock status of the Crevalle Jack in Florida

suggested that B has been below BMSY for 14 of the past

19 years and that the stock is currently undergoing over-

fishing (with F slightly above FMSY). Any increase in

fishing pressure will likely lead to a continued decline

in stock size. Fishing guides in the Florida Keys have

observed a gradual decline in Crevalle Jack catch rates

FIGURE  Results of the sensitivity analysis examining recreational discard mortality (sensitivity analysis 1): (A) time series of catch

(thousands of metric tons) with discard mortality set at 0.0, 0.1, 0.2, 0.3, 0.4, or 0.5; (B) time series of exploitation (fishing mortality F/FMSY,

where MSY = maximum sustainable yield) on the y- axis and stock size (biomass B/BMSY) on the x- axis in the final year (2021) for the range

of discard mortality rates assessed (0.0– 0.5); (C) effect of discard mortality rate on estimated intrinsic population growth rate(r); and (D)

effect of discard mortality rate on estimated carrying capacity (k).

Ye ar

All fishery removals (1000 metric tons)

1950 1957 1964 1971 1978 1985 1992 1999 2006 2013 2020

12 Discard mortality

0.5

0.4

0.3

0.2

0.1

0.0

(A)

Stock size

Exploitation

0.80.9 1.01.1 1.2

0.8

0.9

1.0

1.1

1.2

Discard mortality

0.5

0.4

0.3

0.2

0.1

0.0

(B)

Discard mortality

Intrinsic population growth rate (r)

0.00.1 0.20.3 0.40.5

0.40

0.45

0.50

0.55

0.60 (C)

Discard mortality

Carrying capacity (k)

0.00.1 0.20.3 0.

60 (D)

14 of 22

GERVASI et al.

beginning as early as 1985, with very low catch rates ob-

served since the early 2000s (Gervasi et al.2022b). Our

stock assessment results align with the timing of this

observation and further highlight the need to develop a

management plan for this important fishery.

Crevalle Jack stock status and trends

Our base CMSY– BSM model revealed that the catch of

Florida Crevalle Jack has been at or above MSY almost

every year since 1989, with several years of overfishing

occurring, and that the stock has been in an overfished

state during almost every year since 2003. Stock size has

been gradually declining over time, while recreational

fishing effort appears to be continually increasing. Com-

mercial landings were relatively low throughout the time

series compared to recreational landings, and commercial

landings dropped considerably in the mid- 1990s (coinci-

dent with the commercial gill- net ban in Florida; Smith

et al.2003). The increasing recreational fishing effort is

somewhat surprising, as fishing guides reported that the

Crevalle Jack fishery in the Florida Keys is largely op-

portunistic and catch and release (Gervasi et al. 2022b).

However, in the statewide MRIP data, recreational anglers

report which species were primarily targeted on each fish-

ing trip; out of all Florida trips, Crevalle Jack were reported

as the 46th most targeted species out of 318 species listed

as primary targets. Therefore, the Crevalle Jack is in the top

15% of recreationally targeted species throughout the state.

Studies have shown that recreational landings ex-

ceed commercial landings for many fisheries (Coleman

et al.2004; Radford et al.2018; Lewin et al.2019; Shertzer

et al.2019), and there is growing evidence that recreational

fisheries can be responsible for declines in fish populations

and can have other biological impacts (Lewin et al.2006;

Brownscombe et al. 2019). Even in predominantly catch-

and- release fisheries, postrelease mortality and sublethal

effects on physiology can have substantial impacts on fish

populations (Rudershausen et al.2007; Cooke et al.2013).

Worldwide, the number of recreational anglers (Kear-

ney2002; Pawson et al.2008), the magnitude of recreational

catches (Coleman et al.2004; Felizola Freire et al.2020), and

the economic impact of recreational fishing (Arlinghaus

et al.2019) are increasing. Although recreational fisheries

provide funding for conservation efforts and connect soci-

ety with nature, thereby increasing public awareness and

appreciation of conservation concerns (Griffiths et al.2017;

Arlinghaus et al. 2019; Brownscombe et al. 2019), these

fisheries are prone to high uncertainty, which undermines

sustainable management (Shertzer et al.2019). Appropri-

ate management action that balances the social and ecolog-

ical dimensions of these fisheries is therefore vital.

In addition to increased fishing effort, other factors

may have contributed to the decline and may continue

to impact Crevalle Jack populations in the future. During

LEK interviews, fishing guides were asked to speculate on

potential reasons for the perceived decline in Crevalle Jack

catches, and loss of prey was the most commonly men-

tioned reason (followed by recreational harvest; Gervasi

et al.2022b). Poor water quality, increased predators, and

warmer winters were also potential factors mentioned by

multiple guides. Research has shown that regional climate

variability can lead to changes in the distribution and pro-

ductivity of fish species (Brander2007; Lotze et al.2019).

It is therefore possible that climate- induced shifts in prey

or predator species have contributed to shifts in Crevalle

Jack distributions in the region. Ecosystem- based man-

agement efforts in the South Atlantic and Gulf of Mexico

could contribute to more holistic management of species

such as the Crevalle Jack in the future (e.g., fisheries eco-

system plans; Levin et al.2018).

FIGURE  Results of the base model in comparison with sensitivity analyses 2– 7 (S2– S7): (A) development of predicted relative total

biomass (B/BMSY, where MSY = maximum sustainable yield) for each model run; and (B) time series of exploitation (fishing mortality

F/FMSY) on the y- axis and stock size (B/BMSY) on the x- axis in the final year (2021) for each model run.

15 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

The timing and trajectory of Crevalle Jack exploitation

match the observations of recreational fishing guides in

Florida, some of whom began noticing a decline in Cre-

valle Jack catch rates as early as 1985 (Gervasi et al.2022b).

Most guides, however, noticed the decline in the early to

mid- 2000s, which corresponds to the year when stock

size began dipping below BMSY (2003). Additionally,

guides reported that the decline had been gradual, which

again matches the model results (i.e., even for the anal-

ysis using uninformed priors, stock size declined gradu-

ally from 1970 to 2021). This agreement between fishing

guide observations and model results provides confi-

dence in the stock assessment and highlights the benefits

of incorporating LEK into fisheries research. Consistency

between LEK and other data sources has been observed

in many studies (e.g., Poizat and Baran1997; Aswani and

Hamilton2004; Zukowski et al.2011; Rehage et al.2019;

Santos et al.2019; Bourdouxhe et al.2020), and the use of

LEK in fisheries research and management has increased

substantially over the years (Beaudreau and Levin2014).

A recent study by Shephard et al.(2021) showed that an-

gler LEK matched stock assessment results for four rec-

reational fisheries in Ireland, further demonstrating that

LEK can provide valuable, robust information about fish-

eries stock status and trends.

Importantly, research and management efforts that

rely on stakeholder input and collaboration are most suc-

cessful in situations of mutual trust and respect, which

can be difficult to build and maintain (Thornton and

Scheer2012). In our study, we solicited the aid of experi-

enced recreational fishing guides to fill in knowledge gaps

about the Crevalle Jack fishery and to help inform model

priors. Studies have shown that fishing guides are ideal

research partners, as they have substantial on- the- water

experience and a vested interest in fisheries conservation

(Kroloff et al.2019; Adkins2020; Gervasi et al.2022a). To

ensure continued trust and collaboration, fishing guides

were informed of the results of this study and its potential

management applications. As demonstrated by Gervasi

et al.(2022a), it is important to involve anglers through-

out the scientific research process and beyond to maintain

trusted partnerships. For cases in which there is general

distrust of science and management by key stakeholders,

efforts to build trust and maintain relationships are vital

to conducting LEK research and fisheries co- management

(Thornton and Scheer2012; Rubert- Nason et al.2021).

Sensitivity analyses

Fisheries management is commonly based on setting

target quotas or catch limits based on fisheries reference

points from stock assessments (Newman et al. 2015).

Uncertainty in model parameters that greatly affect the

estimation of reference points can lead to target setting

based on inaccurate estimations of stock status, thus

increasing the risk for either overfishing or underutiliz-

ing the resource (Dankel et al.2012; Cadrin et al. 2015;

Privitera- Johnson and Punt 2020). Sensitivity analysis is

a common approach used by stock assessment scientists

to understand aspects of model uncertainty (Privitera-

Johnson and Punt2020). Compared to our initial CMSY–

BSM model, none of the sensitivity analyses dramatically

altered the overall pattern of exploitation and stock size

over time or the estimated current stock status. In all mod-

els, exploitation increased over time, with harvest increas-

ing to levels at or above MSY at some point during the

time series. Stock size also generally decreased over time,

with overfishing occurring in all models, although the

number of years for which the stock was in an overfished

state varied depending on the model. Exploitation in 2021

was high for all models, with F2021/FMSY ranging from 0.93

to 1.81 (Table3). All models also showed that the stock

in 2021 was overfished (B2021/BMSY < 1). This model con-

sistency reveals high model precision and provides some

additional confidence in our stock assessment results.

However, there still may be unaccounted- for sources

of uncertainty (i.e., “unknown unknowns”; Drouineau

et al.2016) that could affect model accuracy.

Despite the consistency in overall trends among model

runs, estimated management reference points deviated

from the initial model for some of the sensitivity analyses.

Changing the discard mortality rate for our first analysis

had the greatest effect on reference points, and k increased

dramatically with an increase in the discard mortality rate.

This change in k led to a substantial impact on estimated

MSY and BMSY, which are important values needed to de-

termine fishery quotas. This analysis highlights the impor-

tance of estimating an accurate discard mortality rate for

fisheries that are predominantly catch and release. When

angling effort is high, catch- and- release fishing is often ap-

plied as a management solution for reducing angling im-

pacts on important fisheries (Cooke and Schramm2007).

Although catch- and- release fishing can provide many

benefits to fisheries when used appropriately (Arling-

haus et al. 2002, 2007), it can also have unintended and

unaccounted- for consequences (Cooke et al.2002; Cooke

and Suski2005). Several studies have shown that angling

can have a multitude of physiological effects on fish, re-

sulting in morbidity and mortality after release (Cooke

et al. 2002; Campbell et al.2010), and can increase vul-

nerability to predation (Holder et al.2020). Accurately ac-

counting for discard mortality in assessments of largely

catch- and- release fisheries is therefore vital.

Of the remaining sensitivity analyses, using unin-

formed priors had the greatest effect on estimates of k

16 of 22

GERVASI et al.

and BMSY, resulting in a much more optimistic view of

available biomass and stock status. According to this

version of the model, the stock was marginally over-

fished but not undergoing overfishing in 2021. Failure

to provide informed priors could therefore prevent man-

agement action from being taken, potentially leading to

continued overfishing and even stock collapse. Previous

research has shown that Bayesian methods (e.g., BSM)

are highly sensitive to misspecified priors and that well-

thought- out informative priors can considerably reduce

uncertainty (Punt and Hilborn 1997). Expert anglers

have been shown to provide accurate estimates of bio-

mass trends in many studies (Beaudreau and Levin2014;

Shephard et al.2021) and thus serve as a useful resource

for developing informative priors. In fact, research

has shown that synthesizing expert knowledge can be

the most powerful approach for selecting informative

model priors (Punt and Hilborn1997). Previous studies

the specifically employed the CMSY– BSM method have

used expert knowledge to inform the relative biomass

priors required by the model (Demirel et al.2020). The

results of this sensitivity analysis highlight the impor-

tance of the LEK component of our assessment frame-

work (Figure1).

Incorporating effort creep into the model also had

a significant impact on estimated reference points and

stock status. When accounting for a 2% linear increase

in catchability, the model resulted in a much more pes-

simistic view of exploitation and stock status, with B2021

being critically low. Effort creep (also called “technology

creep”) has been shown to significantly alter how fishing

impacts fish stocks (Marchal et al.2007; Scherrer and Gal-

braith2020), but creep factors are typically only estimated

to correct for the introduction of new technologies over

short periods of time. Therefore, applying a blanket effort

creep value to a long- term analysis is not ideal (Palomares

and Pauly2019). Unfortunately, there is a general lack of

quantitative data on the speed and magnitude with which

fishing power changes over time (Engelhard2016). Future

efforts to explicitly quantify changes in catchability due to

advancements in fishing technology could greatly improve

stock assessment models and inform better management.

Finally, excluding high- PSE data points from the MRIP

data also led to a more pessimistic stock status than the

base model due to the anomalously high MRIP catch esti-

mate for 2009. If this was a true spike in catch reflective of

a spike in abundance for that year, its cause is unknown.

A strong recruitment event was a possible cause. It is well

known that variability in juvenile recruitment rates due to

environmental variability can lead to substantial tempo-

ral heterogeneity in population abundance (Shelton and

Mangel2011). However, fishing guides did not mention

any particular spike in Crevalle Jack abundance in 2009 or

anything else that would explain the spike. Additionally,

the PSE of the MRIP estimate was above 50%, meaning

that the estimate was very imprecise. It is therefore more

likely that the catch estimate was based on a small sam-

ple size and is not a “true” reflection of total catch in that

year. Studies have shown that data quantity significantly

impacts stock assessment results (Chen et al.2003). This

sensitivity analysis further highlighted the importance of

considering sample size and the precision of catch esti-

mates when fisheries- dependent data are used to inform

stock assessment.

Implications for management

Our sensitivity analyses revealed some uncertainty in the

extent of overfishing that has occurred since 1950, but all

models showed stock size trending in a negative direction,

suggesting that management action is needed to halt the

decline in stock size. The current exploitation rate is also

at or slightly above MSY. Since the Crevalle Jack is cur-

rently an unregulated species in Florida (FWC2021) and

given that recreational fishing in the region is continually

increasing (Hanson and Sauls2011; Shertzer et al.2019),

it is likely that exploitation rates will continue to increase

to unsustainable levels if the fishery remains unregulated.

Importantly, with recreational fisheries the goal is not

always to maximize yield. Fishing guides in the Florida

Keys have observed that catch rates of Crevalle Jack have

declined below a desirable level in recent years (Gervasi

et al.2022b). Thus, although F2021 was just above FMSY and

B2021 was only slightly below BMSY for most of our model

runs, management regulations that bring catch rates back

up to desirable levels may be more beneficial to the guided

fishery as an industry than managing for MSY. Further

discussions with anglers as to what constitutes a desirable

level of catch will help managers to set appropriate refer-

ence points. Because the Crevalle Jack is an unregulated

species (i.e., there are no species- specific restrictions on

harvest) in all U.S. Gulf and Atlantic states within the spe-

cies' range, additional research into Crevalle Jack stock

structure and stock status in other areas is also a critical

next step.

Our suggested next steps for management include en-

gaging in cooperative research and co- management (Ka-

plan and McCay2004; Johnson and Van Densen2007) and

setting regulations on the Crevalle Jack recreational fish-

ery that are acceptable to the stakeholders and that follow

a precautionary approach. Beyond such steps, additional

research can aid in reducing uncertainty and providing

more concrete management recommendations. The results

of our sensitivity analyses revealed the importance of es-

timating an accurate discard mortality rate since the vast

17 of 22

ASSESSMENT OF AN UNREGULATED FISHERY USING DATA-LIMITED TOOLS

majority of Crevalle Jack captured by recreational anglers

in Florida are released. Tagging studies that assess how

factors such as handling time, hooking location, depth,

and predator abundance influence postrelease survival

will aid in obtaining a better estimate of the survival rate

(Jiang et al.2007; Rudershausen et al.2007; Flaherty- Walia

et al.2016). Accounting for effort creep was also shown to

be incredibly important. Therefore, getting a better handle

on how fishing technology and subsequent catchability of

Crevalle Jack may have changed over time should be an-

other research goal. This could potentially be accomplished

via angler interviews and/or analysis of trends in the adop-

tion and use of new fishing technologies in the region

(e.g., Marchal et al.2007). Finally, accurate delineation of

stock boundaries is an important part of stock assessment

(Ying et al.2011; Berger et al. 2021). Preliminary acous-

tic telemetry research in Florida has revealed that Crevalle

Jack make regular long- range movements throughout the

state and that some individuals even cross state boundaries

into other states within the Gulf of Mexico (C. L. Gervasi,

unpublished data). These results suggest that to encom-

pass the entire stock, the catch and abundance time series

may need to be expanded to include data from other states.

However, according to the MRIP data, approximately 95%

of the Crevalle Jack captured by recreational anglers in

Gulf of Mexico and South Atlantic waters are captured

in Florida. Thus, even if individual fish migrate between

state boundaries, fishing operations in other states are less

likely to impact stock status since the majority of the Cre-

valle Jack fishery operates in Florida. For this reason, a

stock unit extending beyond the state of Florida was not

considered for the assessment conducted herein. However,

as the acoustic telemetry data continue to reveal patterns of

Crevalle Jack movements and migrations, the CMSY– BSM

model could be re- run if necessary to account for changes

in estimated stock boundaries. As new data about the spe-

cies and the fishery are collected, the FishPath assessment

questionnaire can also be updated and other data- limited

assessment methods can be explored and compared. The

three- prong assessment approach outlined herein first uses

the FishPath tool to select an assessment method, then

conducts an assessment using the chosen method, and fi-

nally runs sensitivity analyses for unknown or uncertain

parameters. Local ecological knowledge permeates each

step, rapidly filling in knowledge gaps that would other-

wise take years of additional research and data collection

to fill. This approach can easily be included as part of an

adaptive management plan and can be applied to other un-

regulated species and in other regions.

ACKNOWLEDGMENTS

We are grateful to the recreational fishing guides who

contributed their experiences and expertise to this study

as well as the Lower Keys Guides Association and Florida

Keys Fishing Guides Association for assistance in identi-

fying key informants. We especially thank C. Bradshaw

for information about the Crevalle Jack commercial

fishery and the two anonymous reviewers for comments

that improved the manuscript. Much appreciation goes

to the Nature Conservancy and the creators and devel-

opers of the FishPath tool. We also appreciate R. Froese,

N. Demirel, G. Coro, and H. Winker for developing the

CMSY+ and BSM models and for their thorough docu-

mentation of the method and reproducible code. This

study was developed in collaboration with the Florida

Coastal Everglades Long- Term Ecological Research

Program under National Science Foundation Grant No.

DEB- 1832229, and this work was reviewed and deemed

exempt by the FIU Institutional Review Board (Proto-

col Exemption IRB- 19- 0157; May 7, 2019). This material

is based upon work supported by the National Science

Foundation under Grant No. HRD- 1547798 and Grant

No. HRD- 2111661. These NSF Grants were awarded to

Florida International University as part of the Centers of

Research Excellence in Science and Technology (CREST)

Program. Funding was provided by the Lower Keys

Guides Association, private donation, the Everglades

Foundation FIU ForEverglades Graduate Scholarship,

and the FIU Graduate School Dissertation Year Fellow-

ship. This is Contribution Number 1609 from the Insti-

tute of Environment, a Preeminent Program at Florida

International University.

CONFLICT OF INTEREST STATEMENT

There is no conflict of interest declared in this article.

DATA AVAILABILITY STATEMENT

The data underlying this article will be shared upon rea-

sonable request to the corresponding author.

ETHICS STATEMENT

All ethical guidelines were followed and no animals were

handled in the development of this study.

ORCID

Carissa L. Gervasi https://orcid.

org/0000-0003-1590-9332

Mandy Karnauskas https://orcid.

org/0000-0002-6631-0592

Adyan Rios https://orcid.org/0000-0003-0112-3080

Rolando O. Santos https://orcid.

org/0000-0002-3885-9406

W. Ryan James https://orcid.org/0000-0002-4829-7742

Ryan J. Rezek https://orcid.org/0000-0002-3054-6384

Jennifer S. Rehage https://orcid.

org/0000-0003-0009-6906

18 of 22

GERVASI et al.

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article.

Imagination, Reality, and Reproduction: Comparing Perceptions of Coastal Sea Trout Anglers, Real Catches, and Sea Trout Spawning Activity

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Full-text available

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FISHERIES MANAG ECOL

Sustainable management of recreational fisheries requires balancing angler attitudes and behaviour with fisheries' biological potential. Herein, we investigated the balance in the sea-run brown trout (Salmo trutta) fishery around the Swedish island of Gotland using data collected through a creel survey, test fishing, and spawning returns to selected streams. Anglers accurately anticipated catches of large fish but underestimated the role of natural reproduction in supporting the stock. Female sea trout entering spawning streams and post-spawn sea trout caught on the coast were significantly larger than spawning males and non-spawned fish, and were particularly important for the fishery. Larger sea trout also faced a higher level of harvest pressure than other sizes. Small changes to angler preferences, harvest rates, or regulations could significantly influence characteristics and sustainability of this fishery. The scale and direction of these changes merit further investigation.

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FISHERIES

Flats ecosystems are dynamic, shallow, nearshore marine environments that are interconnected and provide immense ecological and socio-economic benefits. These habitats support a diversity of fish populations and various fisheries, yet they are increasingly threatened by anthropogenic stressors, including overfishing, habitat degradation, coastal development, and the cascading effects of climate change. Effective habitat management and restoration are essential but are often missing for flats ecosystems. Despite navigating a landscape of imperfect knowledge for these systems, decisive action and implementation of habitat protection and restoration is currently needed through policy and practice. We present a comprehensive set of 10 strategic guiding principles necessary for integrating habitat management and restoration for the conservation of interconnected flat ecosystems. These principles include calls for comprehensive ecosystem-based management, integrating adaptive strategies that leverage diverse partnerships, scientific research, legislative initiatives, and local and traditional ecological knowledge. Drawing on successes in other environmental management realms, we emphasize the importance of evidence-informed approaches to address the complexities and uncertainties of flats ecosystems. These guiding principles aim to advance flats habitat management and restoration, promoting ecological integrity and strengthening the socio-economic resilience of these important marine environments.

The stock assessment theory of relativity: deconstructing the term “data-limited” fisheries into components and guiding principles to support the science of fisheries management

Article

Full-text available

Jan 2023
REV FISH BIOL FISHER

The term “data-limited fisheries” is a catch-all to generally describe situations lacking data to support a fully integrated stock assessment model. Data conditions range from data-void fisheries to those that reliably produce quantitative assessments. However, successful fishery assessment can also be limited by resources (e.g., time, money, capacity). The term “data-limited fisheries” is therefore too vague and incomplete to describe such wide-ranging conditions, and subsequent needs for management vary greatly according to each fishery’s context. Here, we acknowledge this relativity and identify a range of factors that can constrain the ability of analyses to inform management, by instead defining the state of being “data-limited” as a continuum along axes of data (e.g., type, quality, and quantity) and resources (e.g., time, funding, capacity). We introduce a tool (the DLMapper) to apply this approach and define where a fishery lies on this relativity spectrum of limitations (i.e. from no data and no resources to no constraints on data and resources). We also provide a ranking of guiding principles, as a function of the limiting conditions. This high-level guidance is meant to identify current actions to consider for overcoming issues associated with data and resource constraints given a specific “data-limited” condition. We apply this method to 20 different fisheries to demonstrate the approach. By more explicitly outlining the various conditions that create “data-limited situations” and linking these to broad guidance, we aim to contextualize and improve the communication of conditions, and identify effective opportunities to continue to develop and progress the science of “limited” stock assessment in support of fisheries management.

Recreational Angler Contributions to Fisheries Management Are Varied and Valuable: Case Studies from South Florida

Article

Full-text available

Nov 2022

Recreational fisheries are complex social–ecological systems that are influenced by a variety of increasing stressors. In Florida (USA), recreational fishing is a US$9.2 billion industry and a vital aspect of the state's culture. However, many targeted species remain unregulated and understudied, which hinders identifying population declines or important ecological interactions. The complex nature of recreational fishing means traditional data collection and analysis methods for assessing stock status and trends are not always appropriate or feasible. Recently, fisheries scientists have begun to recognize the value and importance of angler knowledge, behavior, and collaboration, and how angler‐derived data can complement existing biological data and provide new insights. The purpose of this study was to present an overview of three methods for involving anglers in research and provide case studies from South Florida that demonstrate the success of these approaches for assessing and managing recreational fisheries.

Stock Assessments of the Bottomfish Management Unit Species of Guam, the Commonwealth of the Northern Mariana Islands, and American Samoa, 2019

Technical Report

Full-text available

Aug 2019

Using local ecological knowledge to inform semi-quantitative fishery surveillance indicators: an example in marine recreational angling

Article

Full-text available

Nov 2021

Small scale and recreational fisheries often lack quantitative monitoring, highlighting potential for other reproducible ways of understanding state. The Fisher's Local Ecological Knowledge Surveillance Indicator (FLEKSI) method was developed here to express user's LEK as semi-quantitative fishery SI. FLEKSI was tested using an online questionnaire disseminated in 2021 to capture angler's knowledge of changing marine recreational fisheries around Ireland (Celtic Seas ecoregion). Sets of Likert questions evaluated four fishery descriptors (species, community, catch, and effort). Respondents were asked to report the subregion (Irish Sea, Celtic Sea, or West Coast) in which they usually fish, and the duration (y) of their angling “career.” Likert scores were reported as diverging bar charts, providing SI values that could be evaluated at different spatial or temporal scales. Respondents (N = 657) reported careers of up to > 41 years. ICES assessments of Celtic Seas demersal fish stocks targeted by anglers provide series of SSB/SSBBtrigger against which the FLEKSI SI were compared. FLEKSI suggested long term decline in all four fishery descriptors, and statistical analysis showed that this trend matched ICES assessment outputs averaged across target stocks of cod, pollack, whiting, and sea bass. FLEKSI provides a standardized framework for integrating user's ways of knowing into fishery monitoring.

Bottom-up conservation: using translational ecology to inform conservation priorities for a recreational fishery

Article

Full-text available

Jun 2021

Translational ecology defines a collaborative effort among scientists and stakeholders to rapidly translate environmental problems into action. This approach can be applied in a fisheries management context when information needed to inform regulations is unavailable, yet conservation concerns exist. Our research uses a translational ecology framework to assess the stock status and develop research priorities for the crevalle jack (Caranx hippos) in the Florida Keys, USA, a currently unregulated species. Interview data that compiled expert fishing guide knowledge were used to develop hypotheses tested using existing fisheries-dependent datasets to check for agreement among sources and assess the consistency of observed patterns. Six hypotheses were developed concerning the status and trends of the crevalle jack population in the Florida Keys, and four of these hypotheses received clear support, with agreement between guide observations and one or more of the fisheries-dependent datasets. The results of our study outline an effective translational ecology approach for recreational fisheries management designed to rapidly recognize potential management needs as identified by fishing guides, which allows for actionable science and proactive management.

Ecologist engagement in translational science is imperative for building resilience to global change threats

Article

Full-text available

Apr 2021

The causes and consequences of global change are well-documented, as are mitigation and adaptation strategies. However, human actions continue to fail in building adequate socio-ecological resilience to the accelerating threats of global change. Translational science, which focuses on connecting scientific research to human benefits, is imperative to building resilience to a confluence of global change threats because it brings the implications of theory and empirical research into practice. Translational ecology, an approach to knowledge co-creation that is grounded in equitable, inclusive, empathetic, and just partnerships among administrators, policy makers, scholars, practitioners, and the public, has immense potential to bring about the rapid and expansive social, ecological and political changes necessary to build resilience to global change threats. Here, we articulate a need for greater engagement of ecologists and other professionals in translational initiatives addressing seven major resilience building challenges, and propose a framework that lowers barriers to participation and promotes stronger relationships among stakeholders. We recommend specific actions that ecologists can take based on their situation, as well as evidence and demonstrated need, to foster resilience building through their contributions to communication, policy, education, knowledge creation, leadership, and service as role models. We conclude with an urgent call for expansive engagement of ecologists and other professionals in initiatives that combat misinformation, partner equitably with communities in knowledge creation, cultivate empathy and compassion, bolster public trust in science, and ultimately build decentralized communities of practice that enable rapid and high-impact responses to global change.

Substantial Gaps in the Current Fisheries Data Landscape

Article

Full-text available

Dec 2020

Effective management of aquatic resources, wild and farmed, has implications for the livelihoods of dependent communities, food security, and ecosystem health. Good management requires information on the status of harvested species, yet many gaps remain in our understanding of these species and systems, in particular the lack of taxonomic resolution of harvested species. To assess these gaps we compared the occurrence of landed species (freshwater and marine) from the United Nations Food and Agriculture Organization (FAO) global fisheries production database to those in the International Union for Conservation of Nature (IUCN) Red List and the RAM Legacy Stock Assessment Database, some of the largest and most comprehensive global datasets of consumed aquatic species. We also quantified the level of resolution and trends in taxonomic reporting for all landed taxa in the FAO database. Of the 1,695 consumed aquatic species or groups in the FAO database considered in this analysis, a large portion (35%) are missing from both of the other two global datasets, either IUCN or RAM, used to monitor, manage, and protect aquatic resources. Only a small number of all fished taxa reported in FAO data (150 out of 1,695; 9%) have both a stock assessment in RAM and a conservation assessment in IUCN. Furthermore, 40% of wild caught landings are not reported to the species level, limiting our ability to effectively account for the environmental impacts of wild harvest. Landings of invertebrates (44%) and landings in Asia (>75%) accounted for the majority of harvest without species specific information in 2018. Assessing the overlap of species which are both farmed and fished to broadly map possible interactions – which can help or hinder wild populations - we found 296 species, accounting for 12% of total wild landings globally, and 103 countries and territories that have overlap in the species caught in the wild and produced through aquaculture. In all, our work highlights that while fisheries management is improving in many areas there remain key gaps in data resolution that are critical for fisheries assessments and conservation of aquatic systems into the future.

Age, growth and diet of crevalle jack (Caranx hippos) in the Gulf of Mexico

Article

Apr 2022

The goals of this study were to generate baseline population dynamics parameters for Gulf of Mexico crevalle jack Caranx hippos and examine the foraging habits of Mississippi and Alabama crevalle jack. Specimens were collected from Mississippi, Alabama and Florida, and age was estimated from sagittal otoliths. Stomachs from some specimens were retained for dietary analyses. Age classes spanned 0–20 years. Overall growth was best represented by the logistic growth model, whereas sex‐specific growth was best represented by a version of the von Bertalanffy growth function that allowed L∞ to vary by sex while holding k and t0 constant between sexes. Fishes were more important to crevalle jack diet than invertebrates, and diet varied among locations and years. These findings will address fundamental knowledge gaps to inform age‐based stock assessments for crevalle jack and ecosystem approaches to fisheries management in the Gulf of Mexico.

Collating stock assessment packages to improve stock assessments

Article

Apr 2021
FISH RES

Stock assessments are integral to fisheries management, and the demand for stock assessments based on population dynamics models continues to increase. Historically, stock assessments have been based on bespoke methods and software. There is now a trend towards the use of flexible, documented, tested and maintained software packages, because use of such packages increases efficiency and consistency in assessments and should lead to more reliable and repeatable assessment outcomes. We have developed a web-site that collates available stock assessment packages (from data-limited to -rich) to provide assessment analysts with a single platform that summarizes the features of the available packages in a consistent manner. We also highlight current state-of-the-art packages, and those that are no longer supported or have been superseded. In addition to providing the packages, the web-site facilitates users of packages to provide feedback to developers regarding which features of other packages could be included in future versions of their packages. We see this effort as a way to support the design of the next generation stock assessment package.

Incoherent dimensionality in fisheries management: consequences of misaligned stock assessment and population boundaries

Article

Dec 2020

Fisheries policy inherently relies on an explicit definition of management boundaries that delineate the spatial extent over which stocks are assessed and regulations are implemented. However, management boundaries tend to be static and determined by politically negotiated or historically identified population (or multi-species) units, which create a potential disconnect with underlying, dynamic population structure. The consequences of incoherent management and population or stock boundaries were explored through the application of a two-area spatial simulation–estimation framework. Results highlight the importance of aligning management assessment areas with underlying population structure and processes, especially when fishing mortality is disproportionate to vulnerable biomass among management areas, demographic parameters (growth and maturity) are not homogenous within management areas, and connectivity (via recruitment or movement) unknowingly exists among management areas. Bias and risk were greater for assessments that incorrectly span multiple population segments (PSs) compared to assessments that cover a subset of a PS, and these results were exacerbated when there was connectivity between PSs. Directed studies and due consideration of critical PSs, spatially explicit models, and dynamic management options that help align management and population boundaries would likely reduce estimation biases and management risk, as would closely coordinated management that functions across population boundaries.

Rapid approach for assessing an unregulated fishery using a series of data‐limited tools

Abstract

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